CN105809641B - The exposure compensating and edge enhancing method of a kind of mist elimination image - Google Patents
The exposure compensating and edge enhancing method of a kind of mist elimination image Download PDFInfo
- Publication number
- CN105809641B CN105809641B CN201610132835.1A CN201610132835A CN105809641B CN 105809641 B CN105809641 B CN 105809641B CN 201610132835 A CN201610132835 A CN 201610132835A CN 105809641 B CN105809641 B CN 105809641B
- Authority
- CN
- China
- Prior art keywords
- mrow
- image
- layer
- mtd
- luminance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 30
- 230000002708 enhancing effect Effects 0.000 title abstract description 3
- 230000008030 elimination Effects 0.000 title abstract 5
- 238000003379 elimination reaction Methods 0.000 title abstract 5
- 239000003595 mist Substances 0.000 title abstract 5
- 238000002834 transmittance Methods 0.000 claims description 19
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000006243 chemical reaction Methods 0.000 claims description 5
- 230000000694 effects Effects 0.000 claims description 4
- 238000009499 grossing Methods 0.000 claims description 4
- 230000005540 biological transmission Effects 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 3
- 238000007499 fusion processing Methods 0.000 claims description 2
- 230000009466 transformation Effects 0.000 abstract 1
- 101100014407 Pisum sativum GDCSP gene Proteins 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/73—Deblurring; Sharpening
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
- Studio Devices (AREA)
Abstract
The invention discloses a kind of exposure compensating of mist elimination image and edge enhancing method, a kind of improved colour killing algorithm based on significance guiding is combined with gamma transformation, brightness layer to mist elimination image is modified compensation, the problem of so as to effectively improve mist elimination image under-exposure;And the method by carrying out marginal information compensation to the input item of Steerable filter device, the problem of effectively improving mist elimination image edge blurry.
Description
Technical Field
The invention belongs to the field of digital image processing, and particularly relates to an exposure compensation and edge enhancement method for a defogged image.
Background
Images and videos shot in foggy weather or dust-haze weather often have the phenomenon of poor image quality and low definition. In order to improve the quality and the definition of the image, the image needs to be defogged.
The image defogging method based on dark channel priority (dark channel prior) is one of the defogging methods based on the fog model, and achieves better effect in practice. Since the transmittance estimation is low in this method and needs to be optimized by a soft-matting method, the main disadvantages are high computational complexity and dark defogged images, i.e., underexposure. In order to increase the calculation speed, the coarse transmittance estimation can be optimized by using a guiding filter, but in the case that the guiding filter is a fog image with a blurred guiding image, the obtained defogged image generates an edge blurring phenomenon.
In view of the above analysis, the major drawbacks of the method using a combination of dark channel precedence and guided filters (i.e., GDCP defogging algorithm) are manifested as underexposure and edge blurring.
Disclosure of Invention
In view of the above, the present invention provides an exposure compensation and edge enhancement method for a defogged image, which can effectively improve the problems of underexposure and edge blurring of the defogged image.
The technical scheme for realizing the invention is as follows:
an exposure compensation and edge enhancement method of a defogged image comprises the following steps:
step one, calculating a dark channel I of a fog image I to be processed according to the definition of the dark channel prioritydark(ii) a And according to the dark channel IdarkEstimating atmospheric light A;
step two, calculating a dark channel of the I/A according to the atmospheric light A obtained by estimation; estimation of initial transmission from dark channels of I/AFor initial transmittancePerforming Gaussian smoothing to obtain smooth transmittance ts;
Step three, solving the Laplacian pyramid L of the 0 th layer of the fog image I by adopting an image pyramid technology0And using the formula (3) for the smooth transmittance tsOptimizing to obtain te:
Wherein,indicating guided filtering, ξ is a preset adjustmentThe ratio parameter of (a) to (b),is the Laplacian pyramid L of the 0 th layer of the fog image I0The luminance image of (a);
step four, obtaining the atmospheric light A and t according to estimationeCalculate the initial defogged image JL:
Step five, decomposing an initial defogged image JLAnd a luminance layer L, calculating to obtain a luminance image according to the luminance layer LTo pairStandardizing and fusing to obtain achromatic image Idec:
Step six, canceling image IdecPerforming gamma conversion to obtain L';
step seven, maintaining the initial defogged image JLAnd (3) replacing the brightness layer L of the image with the L' to reconstruct the final defogged image.
Further, the method for estimating the atmospheric light a comprises: taking a dark channel I of a fog image I to be processeddarkTaking the pixel set B with the maximum brightness of 0.1%, and then taking the pixel set B corresponding to the pixel set B in the original image IIFrom BIAnd selecting three channels of RGB and the largest pixel point as the estimation of A.
Further, the initial transmittanceThe estimation method comprises the following steps:
where ω is a constant close to 1 but less than 1, c is one of the color channels { r, g, b } of the fog image I; Ω is a local window area centered on x, y denotes the pixels in the Ω window, Ic(y) represents the value of the c-th color layer of the fog image I at y, AcAnd the value of the c color layer of A is shown.
Further, the luminance image is obtained through calculation according to the luminance layer LThe method specifically comprises the following steps:
wherein G [. C]Is a gaussian feathering operator; mmask(x) Represents MmaskTaking a value at the point x; wherein S (x) is not less than mu and L (x) is not less than v; h is a chrominance layer, S is a saturation layer; the parameter k is a preset period adjustment parameter,is a compensation angle, τ is a preset parameter for reducing the effect of saturation; μ and v are preset thresholds; n is the number of pixels in the region Ω, s (x) represents the saturation at point x, and l (x) represents the luminance at point x.
Further, theThe standardization and fusion processing methods are respectively as follows:
wherein,for normalizing luminance image, χ is a predetermined parameter for adjusting global intensity to avoid overexposure or underexposure of the image, η is a predetermined control L andthe mixing ratio parameter of (1).
Has the advantages that:
the invention provides an improved algorithm aiming at exposure compensation and edge enhancement of a GDCP defogging algorithm. In the invention, an improved achromatism algorithm based on saliency guidance is combined with gamma conversion to correct and compensate the brightness layer of the defogged image, thereby effectively improving the problem of insufficient exposure of the defogged image; and the problem of edge blurring of the defogged image is effectively improved by a method of performing edge information compensation on the input item of the guide filter.
Drawings
FIG. 1 is a schematic flow chart of an image defogging method according to the present invention.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
As shown in fig. 1, the present invention provides an exposure compensation and edge enhancement method for a defogged image, comprising the steps of:
step one, calculating a dark channel I of a fog image I to be processed according to the definition of the dark channel prioritydark(ii) a And according to the dark channel I of the fog image IdarkEstimating atmospheric light A;
the calculation formula for the dark channel can be expressed as:
wherein c is one of the color channels { r, g, b } of the fog image I; Ω is a local window area centered on x, and y represents the pixels in the Ω window; i isdark(x) I.e. the dark channel map of i (x). I isc(y) represents the value of the c-th color layer of the fog image I at y.
The method for estimating the atmospheric light A comprises the following steps: taking a dark channel I of a fog image I to be processeddarkTaking the pixel set B with the maximum brightness of 0.1%, and then taking the pixel set B corresponding to the pixel set B in the original image IIFrom BIAnd selecting three channels of RGB and the largest pixel point as the estimation of A.
Step two, calculating a dark channel of the I/A according to the atmospheric light A obtained by estimation; estimation of initial transmission from dark channels of I/AFor initial transmittancePerforming Gaussian smoothing to obtain smooth transmittance ts;
Initial transmittanceThe calculation formula of (2) is as follows:
where ω is a constant near 1 but less than 1. At this time, ω is equivalent to moderately preserving a certain depth of field for distant scenes. In general, ω is preferably set to 0.95. A. thecAnd the value of the c color layer of A is shown.
Due to initial transmittance of the imageThere is a block effect, here on the initial transmittance, in order to reduce the transfer of information at the edges of these blocksAnd performing Gaussian smoothing processing.
Step three, solving the Laplacian pyramid L of the 0 th layer of the fog image I by adopting an image pyramid technology0And using the formula (3) for the smooth transmittance tsOptimizing to obtain te:
Wherein,indicating guided filtering, ξ is a preset adjustmentξ in the present invention takes the value of 1.Is the Laplacian pyramid L of the 0 th layer of the fog image I0Luminance image of (1), i.e. L0Average value of each color channel.
In fact, the root cause of the haze image blur caused by the GDCP defogging algorithm is that in the optimization process of transmittance by using the guide filter, the guide image is the fog image i (x), and i (x) is blurred and the edge information is not obvious enough. Therefore, the Laplacian pyramid capable of reflecting the edge information is added to the input item of the guide filter to compensate the edge information. Here, the luminance image L of the 0 th layer laplacian pyramid is selected0Because of the need for and smooth transmittance tsThe resolution of (c) is the same.
Since the guiding filter is a local linear filter, the transmittance image after enhancing details can be expressed as:
wherein,
this means that the steering filter needs to be computed only once, and thus does not add computational complexity while edge enhancement.
Step four, obtaining the atmospheric light A and t according to estimationeCalculate the initial defogged image JL:
The calculation formula is as follows:
step five, decomposing an initial defogged image JLThe luminance layer L of (1), the luminance image is obtained according to the formulas (6) and (7)To pairStandardizing and fusing to obtain achromatic image Idec:
Wherein G [. C]Is a gaussian feathering operator; mmask(x) Represents MmaskTaking a value at the point x; wherein S (x) is not less than mu and L (x) is not less than v; h is a chrominance layer, S is a saturation layer; the parameter k is a preset periodic adjustment parameter, which can maintain the opposite color,is a compensation angle, tau is a preset parameter for reducing the influence of saturation, and the function is equivalent to controlling a chroma contrast modulator; μ and v are preset thresholds used to filter discontinuities in the salient region; in the present invention, the default values of these two parameters are: mu is 0.1, v is 0.6
N is the number of pixels in the region Ω, s (x) represents the saturation at point x, and l (x) represents the luminance at point x;
wherein,for normalizing luminance images, the parameter χ is used to adjust global intensity to avoid overexposure or underexposure of the image, η is the sum of controls L andthe mixing ratio parameter of (1).
One drawback of the saliency-based guided achromatic algorithm is that when smooth bright areas exist in the image, the computed achromatic image has discontinuities. Here, fusing these two discontinuous regions can effectively eliminate the discontinuous edges.
Step six, canceling image IdecPerforming gamma conversion to obtain L';
step seven, maintaining the initial defogged image JLAnd (3) replacing the brightness layer L of the image with the L' to reconstruct the final defogged image.
And from the fifth step to the seventh step, exposure compensation is carried out on the initial defogged image. The conventional exposure compensation method is to apply a simple gamma transform to the defogged image. As a result of this process, although the overall brightness of the image is improved, the chrominance contrast of the image is affected. According to the exposure compensation method, before gamma conversion is adopted, the chroma of an original defogged image is enhanced by using a decoloring algorithm based on saliency guidance. Thus, the image after exposure compensation can better maintain the chroma contrast of the original image.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. An exposure compensation and edge enhancement method for a defogged image, comprising the steps of:
step one, calculating a dark channel I of a fog image I to be processeddark(ii) a And according to the dark channel IdarkEstimating atmospheric light A;
step two, calculating a dark channel of the I/A according to the atmospheric light A obtained by estimation; estimation of initial transmission from dark channels of I/AFor initial transmittancePerforming Gaussian smoothing to obtain smooth transmittance ts;
Step three, solving the Laplacian pyramid L of the 0 th layer of the fog image I by adopting an image pyramid technology0And using the formula (3) for the smooth transmittance tsOptimizing to obtain te:
Wherein,indicating guided filtering, ξ is a preset adjustmentThe ratio parameter of (a) to (b),is the Laplacian pyramid L of the 0 th layer of the fog image I0The luminance image of (a);
step four, obtaining the atmospheric light A and t according to estimationeCalculate the initial defogged image JL:
Step five, decomposing an initial defogged image JLAnd a luminance layer L, calculating to obtain a luminance image according to the luminance layer LTo pairStandardizing and fusing to obtain achromatic image Idec:
The brightness image is obtained by calculation according to the brightness layer LThe method specifically comprises the following steps:
<mrow> <msub> <mi>M</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>s</mi> <mi>k</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>G</mi> <mo>&lsqb;</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mrow> <mi>S</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>&le;</mo> <mi>&mu;</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mi> </mi> <mi>L</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>&GreaterEqual;</mo> <mi>v</mi> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <mi>o</mi> <mi>t</mi> <mi>h</mi> <mi>e</mi> <mi>r</mi> <mi>w</mi> <mi>i</mi> <mi>s</mi> <mi>e</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>&rsqb;</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow>
wherein G [. C]Is a gaussian feathering operator; mmask(x) Represents MmaskTaking a value at the point x; wherein S (x) is not less than mu and L (x) is not less than v; h is a chrominance layer, S is a saturation layer; the parameter k is a preset period adjustment parameter,is a compensation angle, τ is a preset parameter for reducing the effect of saturation; μ and v are preset thresholds; n is the number of pixels in the region Ω, s (x) represents the saturation at point x, and l (x) represents the luminance at point x;
the standardization and fusion processing methods are respectively as follows:
wherein,for normalizing luminance image, χ is a predetermined parameter for adjusting global intensity to avoid overexposure or underexposure of the image, η is a predetermined control L andthe mixing ratio parameter of (1);
step six, canceling image IdecPerforming gamma conversion to obtain L';
step seven, maintaining the initial defogged image JLReplacing the luminance layer L of the image with L', and repeatingAnd forming a final defogged image.
2. The exposure compensation and edge enhancement method for a defogged image according to claim 1, wherein the atmospheric light A is estimated by: taking a dark channel I of a fog image I to be processeddarkTaking the pixel set B with the maximum brightness of 0.1%, and then taking the pixel set B corresponding to the pixel set B in the original image IIFrom BIAnd selecting three channels of RGB and the largest pixel point as the estimation of A.
3. The exposure compensation and edge-enhancement method of a defogged image according to claim 1, wherein an initial transmittanceThe estimation method comprises the following steps:
<mrow> <mover> <mi>t</mi> <mo>~</mo> </mover> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mn>1</mn> <mo>-</mo> <mi>&omega;</mi> <munder> <mi>min</mi> <mrow> <mi>c</mi> <mo>&Element;</mo> <mo>{</mo> <mi>r</mi> <mo>,</mo> <mi>g</mi> <mo>,</mo> <mi>b</mi> <mo>}</mo> </mrow> </munder> <mrow> <mo>(</mo> <munder> <mi>min</mi> <mrow> <mi>y</mi> <mo>&Element;</mo> <mi>&Omega;</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> </mrow> </munder> <mo>(</mo> <mfrac> <mrow> <msup> <mi>I</mi> <mi>c</mi> </msup> <mrow> <mo>(</mo> <mi>y</mi> <mo>)</mo> </mrow> </mrow> <msup> <mi>A</mi> <mi>c</mi> </msup> </mfrac> <mo>)</mo> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow>
where ω is a constant close to 1 but less than 1, c is one of the color channels { r, g, b } of the fog image I; Ω is a local window area centered on x, y denotes the pixels in the Ω window, Ic(y) represents the value of the c-th color layer of the fog image I at y, AcAnd the value of the c color layer of A is shown.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201610132835.1A CN105809641B (en) | 2016-03-09 | 2016-03-09 | The exposure compensating and edge enhancing method of a kind of mist elimination image |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201610132835.1A CN105809641B (en) | 2016-03-09 | 2016-03-09 | The exposure compensating and edge enhancing method of a kind of mist elimination image |
Publications (2)
Publication Number | Publication Date |
---|---|
CN105809641A CN105809641A (en) | 2016-07-27 |
CN105809641B true CN105809641B (en) | 2018-02-16 |
Family
ID=56466896
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201610132835.1A Active CN105809641B (en) | 2016-03-09 | 2016-03-09 | The exposure compensating and edge enhancing method of a kind of mist elimination image |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN105809641B (en) |
Families Citing this family (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108257094A (en) * | 2016-12-29 | 2018-07-06 | 广东中科遥感技术有限公司 | The quick minimizing technology of remote sensing image mist based on dark |
CN109636765B (en) * | 2018-11-09 | 2021-04-02 | Tcl华星光电技术有限公司 | High dynamic display method based on image multiple exposure fusion |
CN110611750B (en) * | 2019-10-31 | 2022-03-22 | 北京迈格威科技有限公司 | Night scene high dynamic range image generation method and device and electronic equipment |
CN111738959B (en) * | 2020-06-30 | 2022-08-19 | 福州大学 | Real-time defogging method for video image based on FPGA |
CN112200755B (en) * | 2020-12-09 | 2021-05-07 | 成都索贝数码科技股份有限公司 | Image defogging method |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103413305A (en) * | 2013-08-01 | 2013-11-27 | 北京航空航天大学 | Single-image rapid defogging method and device and image processing system |
KR101426298B1 (en) * | 2014-01-24 | 2014-08-13 | (주)인펙비전 | apparatus and method for compensating image for enhancing fog removing efficiency |
CN103150708B (en) * | 2013-01-18 | 2015-11-18 | 上海交通大学 | Based on the image Quick demisting optimization method of black channel |
CN105205793A (en) * | 2015-09-23 | 2015-12-30 | 成都融创智谷科技有限公司 | Image defogging method based on dark channel prior |
-
2016
- 2016-03-09 CN CN201610132835.1A patent/CN105809641B/en active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103150708B (en) * | 2013-01-18 | 2015-11-18 | 上海交通大学 | Based on the image Quick demisting optimization method of black channel |
CN103413305A (en) * | 2013-08-01 | 2013-11-27 | 北京航空航天大学 | Single-image rapid defogging method and device and image processing system |
KR101426298B1 (en) * | 2014-01-24 | 2014-08-13 | (주)인펙비전 | apparatus and method for compensating image for enhancing fog removing efficiency |
CN105205793A (en) * | 2015-09-23 | 2015-12-30 | 成都融创智谷科技有限公司 | Image defogging method based on dark channel prior |
Non-Patent Citations (2)
Title |
---|
Single Image Haze Removal Using Dark Channel Prior;Kaiming He 等;《IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE》;20111231;第33卷(第12期);第2341-2353页 * |
基于曝光融合的单幅图像去雾算法;唐鉴波 等;《计算机应用》;20140310;第34卷(第3期);第820-823页 * |
Also Published As
Publication number | Publication date |
---|---|
CN105809641A (en) | 2016-07-27 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN105809641B (en) | The exposure compensating and edge enhancing method of a kind of mist elimination image | |
CN110148095B (en) | Underwater image enhancement method and enhancement device | |
CN106780368B (en) | Underwater image enhancement method based on foreground model | |
CN106251300B (en) | A kind of quick night Misty Image restored method based on Retinex | |
WO2016206087A1 (en) | Low-illumination image processing method and device | |
Gao et al. | Sand-dust image restoration based on reversing the blue channel prior | |
CN106530257A (en) | Remote sensing image de-fogging method based on dark channel prior model | |
CN108765342A (en) | A kind of underwater image restoration method based on improvement dark | |
CN112419162A (en) | Image defogging method and device, electronic equipment and readable storage medium | |
CN109658343B (en) | Underwater image enhancement method combining color conversion and adaptive exposure | |
CN102750674A (en) | Video image defogging method based on self-adapting allowance | |
CN104867121B (en) | Image Quick demisting method based on dark primary priori and Retinex theories | |
CN104036466A (en) | Video defogging method and system | |
CN106886985A (en) | A kind of self adaptation enhancement method of low-illumination image for reducing colour cast | |
CN110827225A (en) | Non-uniform illumination underwater image enhancement method based on double exposure frame | |
CN106570838A (en) | Image brightness optimization method and device | |
CN104331867B (en) | The method, device and mobile terminal of image defogging | |
CN104091307A (en) | Frog day image rapid restoration method based on feedback mean value filtering | |
CN110009574B (en) | Method for reversely generating high dynamic range image from low dynamic range image | |
CN107527325A (en) | A kind of monocular underweater vision Enhancement Method preferential based on dark | |
CN112053298A (en) | Image defogging method | |
CN115187472A (en) | Dark channel prior defogging method based on tolerance | |
CN107977941B (en) | Image defogging method for color fidelity and contrast enhancement of bright area | |
CN109345479B (en) | Real-time preprocessing method and storage medium for video monitoring data | |
CN104299195B (en) | A kind of image defogging method based on auto-adaptive doublethreshold and dark channel prior |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |